Q-Margin encodes margin penalties into the reference measure of an alpha-divergence loss to produce sparse discriminative embeddings for face and speaker verification.
Arcface: Additive angular margin loss for deep face recognition
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
A diffusion-based contrastive analysis method that decomposes conditioning into common and salient factors with weak supervision and proves identifiability of the additive model.
Human face perception aligns with neural networks trained on inverse-generative and naturalistic discriminative tasks, as these best predict human dissimilarity judgments on controversial and random face pairs.
ARFP is a key-conditioned reversible face cloaking method that resists unauthorized restoration attacks while enabling authorized recovery with tamper indication.
Any3DAvatar reconstructs full-head 3D Gaussian avatars from one image via one-step denoising on a Plücker-aware scaffold plus auxiliary view supervision, beating prior single-image methods on fidelity while running substantially faster.
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
Empirical evaluation shows age estimation models perform orders of magnitude below identification thresholds on face verification benchmarks, indicating they do not extract identity-discriminative representations.
citing papers explorer
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Sparsity-Inducing Divergence Losses for Biometric Verification
Q-Margin encodes margin penalties into the reference measure of an alpha-divergence loss to produce sparse discriminative embeddings for face and speaker verification.
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Diff-CA: Separating Common and Salient Factors with Diffusion Models
A diffusion-based contrastive analysis method that decomposes conditioning into common and salient factors with weak supervision and proves identifiability of the additive model.
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Human face perception reflects inverse-generative and naturalistic discriminative objectives
Human face perception aligns with neural networks trained on inverse-generative and naturalistic discriminative tasks, as these best predict human dissimilarity judgments on controversial and random face pairs.
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Asymmetric Invertible Threat: Learning Reversible Privacy Defense for Face Recognition
ARFP is a key-conditioned reversible face cloaking method that resists unauthorized restoration attacks while enabling authorized recovery with tamper indication.
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Any3DAvatar: Fast and High-Quality Full-Head 3D Avatar Reconstruction from Single Portrait Image
Any3DAvatar reconstructs full-head 3D Gaussian avatars from one image via one-step denoising on a Plücker-aware scaffold plus auxiliary view supervision, beating prior single-image methods on fidelity while running substantially faster.
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Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
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Position: Age Estimation Models Do Not Process Biometric Data
Empirical evaluation shows age estimation models perform orders of magnitude below identification thresholds on face verification benchmarks, indicating they do not extract identity-discriminative representations.